{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook illustrate how to permform a Leave p-subjects out (usually, people working in EEG/MEG do a Leav 1-subject out). This notebook will use some notions introduce in the Classification.ipynb notebook\n",
    "Documentation: https://etiennecmb.github.io/classification.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import librairies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "# u can use %matplotlib notebook, but there is some bugs with xticks and title\n",
    "\n",
    "from brainpipe.classification import *\n",
    "from brainpipe.visual import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a random dataset\n",
    "We are going to create a random dataset for a 2 class problem, with n_feature for 4 subjects and a diffrent number of trials for each subject. The quality of decoding of features will be increasing with the ranking, meaning that the first feature is going to be a bad one, the second alittle bit better, the third..., the last, the best one.\n",
    "## Dataset settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_features = 15   # Number of features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jXAMAwECEawAAGIhwDQAAAxGuAQBgIMI1AAAMRLgGAICBCNcAADAQ4RoAAAYiXAMAwECE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea3fcfaeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def dataset_pear_subject(ntrials):\n",
    "    \"\"\"Create a dataset for each subject. This little function\n",
    "    will return x and y, the dataset and the label vector of each subject\n",
    "    \"\"\"\n",
    "    spread = np.linspace(0, 0.8, n_features)\n",
    "    class1 = np.random.uniform(size=(ntrials, n_features)) + spread\n",
    "    class2 = np.random.uniform(size=(ntrials, n_features)) - spread\n",
    "    x = np.concatenate((class1, class2), axis=0)\n",
    "    y = np.ravel([[k]*ntrials for k in np.arange(2)])\n",
    "    return x, y\n",
    "\n",
    "# Create a random dataset and a label vector for each subject\n",
    "x_s1, y_s1 = dataset_pear_subject(20)   # 20 trials for subject 1\n",
    "x_s2, y_s2 = dataset_pear_subject(25)   # 25 trials for subject 2\n",
    "x_s3, y_s3 = dataset_pear_subject(18)   # 18 trials for subject 3\n",
    "x_s4, y_s4 = dataset_pear_subject(10)   # 10 trials for subject 4\n",
    "\n",
    "# Concatenate all datasets and vectors in list :\n",
    "x = [x_s1, x_s2, x_s3, x_s4]\n",
    "y = [y_s1, y_s2, y_s3, y_s4]\n",
    "\n",
    "# Plot each subject dataset:\n",
    "plt.figure(0, figsize=(12,6))\n",
    "plt.boxplot(x);\n",
    "rmaxis(plt.gca(), ['top', 'right']);\n",
    "plt.xlabel('Subjects'), plt.ylabel('Values');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification\n",
    "## Define a classifier and the leave p-subject out cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Classification object :\n",
    "lpso = LeavePSubjectOut(y, 4, pout=1, clf='svm', kern='linear') # Leave ONE-subject out (pout)\n",
    "\n",
    "# Run classification :\n",
    "da, pvalue, daperm = lpso.fit(x, n_perm=200, method='label_rnd')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot your results\n",
    "### Plot decoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Settings</th>\n",
       "      <th colspan=\"5\" halign=\"left\">SVM-linear / Leave 1 subjects out</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Results</th>\n",
       "      <th>DA (%)</th>\n",
       "      <th>STD (+/-)</th>\n",
       "      <th>p-values (Binomial)</th>\n",
       "      <th>p-values (Permutations)</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54.109589</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.409683e-01</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60.958904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.059352e-03</td>\n",
       "      <td>0.020</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66.438356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.133173e-05</td>\n",
       "      <td>0.005</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72.602740</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.156421e-09</td>\n",
       "      <td>0.005</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74.657534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.273628e-10</td>\n",
       "      <td>0.005</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>75.342466</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.044143e-10</td>\n",
       "      <td>0.005</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>86.301370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>0.005</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>86.986301</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>0.005</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>95.205479</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>0.005</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99.315068</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>0.005</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.005</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.005</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.005</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.005</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.005</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Settings SVM-linear / Leave 1 subjects out                                \\\n",
       "Results                             DA (%) STD (+/-) p-values (Binomial)   \n",
       "0                                54.109589       0.0        1.409683e-01   \n",
       "1                                60.958904       0.0        3.059352e-03   \n",
       "2                                66.438356       0.0        2.133173e-05   \n",
       "3                                72.602740       0.0        8.156421e-09   \n",
       "4                                74.657534       0.0        3.273628e-10   \n",
       "5                                75.342466       0.0        1.044143e-10   \n",
       "6                                86.301370       0.0        1.110223e-16   \n",
       "7                                86.986301       0.0        1.110223e-16   \n",
       "8                                95.205479       0.0        1.110223e-16   \n",
       "9                                99.315068       0.0        1.110223e-16   \n",
       "10                              100.000000       0.0        0.000000e+00   \n",
       "11                              100.000000       0.0        0.000000e+00   \n",
       "12                              100.000000       0.0        0.000000e+00   \n",
       "13                              100.000000       0.0        0.000000e+00   \n",
       "14                              100.000000       0.0        0.000000e+00   \n",
       "\n",
       "Settings                                \n",
       "Results  p-values (Permutations) Group  \n",
       "0                          0.280   0.0  \n",
       "1                          0.020   1.0  \n",
       "2                          0.005   2.0  \n",
       "3                          0.005   3.0  \n",
       "4                          0.005   4.0  \n",
       "5                          0.005   5.0  \n",
       "6                          0.005   6.0  \n",
       "7                          0.005   7.0  \n",
       "8                          0.005   8.0  \n",
       "9                          0.005   9.0  \n",
       "10                         0.005  10.0  \n",
       "11                         0.005  11.0  \n",
       "12                         0.005  12.0  \n",
       "13                         0.005  13.0  \n",
       "14                         0.005  14.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Sg4GVwL7jqE+SJEmaK+MaUkJVHQ4cPmX2xXTDTSRJkqQlwTtNSpIkSQ0ZuCVJ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\nd0RERETEOErBHRERERExjv4PPVUiX3bWoT8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea3fd200f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=(12,8))\n",
    "\n",
    "lpso.daplot(da, daperm=daperm, chance_method='perm', rmax=['top', 'right'],\n",
    "            dpax=['bottom', 'left'], cmap='magma', ylim=[40,100], chance_unique=False,\n",
    "            chance_color='darkgreen')\n",
    "\n",
    "# Display informations about features :\n",
    "lpso.info.featinfo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A little bit of stat (not to much)\n",
    "Ok, you have your true decoding accuracies, the pvalues and the decoding of all permutations. Inside the classification object lpso, there is some sub-methods for advanced statistics. So, take a look at lpso.stat.\n",
    "\n",
    "As an example, say that you need to find the p=0.05 corresponding decoding accuracy in the permutation and for each feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fea3d854be0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+Y7bRcfLM3rus5NhYzq5di5ePDzX79XNFtGy1/bf1ue69X39NwsWLLh37b8sr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\nUi7s/wFL1pRL0po9/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea3d8ef588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# p<0.05\n",
    "dap05 = lpso.stat.perm_pvalue2da(daperm, p=0.05)\n",
    "# p<0.01\n",
    "dap01 = lpso.stat.perm_pvalue2da(daperm, p=0.01)\n",
    "# p<0.01 with maximum statistics correction\n",
    "dap_01corrected = lpso.stat.perm_pvalue2da(daperm, p=0.01, maxst=True)\n",
    "\n",
    "plt.figure(2, figsize=(8, 6))\n",
    "plt.plot(dap05, lw=2, color='darkblue', label='p<0.05')\n",
    "plt.plot(dap01, lw=2, color='darkred', label='p<0.01')\n",
    "plt.plot(dap_01corrected, lw=2, color='darkgreen', label='p<0.01 corrected')\n",
    "plt.xlabel('Features'), plt.ylabel('Decoding accuracy')\n",
    "plt.legend(loc=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grouping witth the Leave p-subject out\n",
    "## The group parameter and multi-features\n",
    "Previously, we saw how to classify each feature separatly. Now we are going to see how to group features. In this example, we defined 15 features. So, we are going to define 3 groups features\n",
    "- 'Group1: the bad one': the 5 first\n",
    "- 'Group2: the middle one': the 3 following\n",
    "- 'Group3: the best one': the last 7 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Define the group parameter :\n",
    "grp = ['Group1: the bad one']*5 + ['Group2: the middle one']*3 + ['Group3: the best one']*7\n",
    "\n",
    "# Change the current classifier :\n",
    "da2, pvlue2, daperm2 = lpso.fit(x, n_perm=50, grp=grp, method='label_rnd', center=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the grouping decoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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h28aaiFkl/T2/nvuURqn71LZpsDgdkl/7a/kx89Rtu66k2/NrGyNpV0l7kQYB+Ul+/svr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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea3d92c240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(3, figsize=(8, 6))\n",
    "lpso.daplot(da2, cmap='Spectral_r', ylim=[45, 100], chance_method='perm',\n",
    "                daperm=daperm2, chance_color='darkgreen');\n",
    "lpso.info.featinfo\n",
    "\n",
    "# Export tables to a excel file:\n",
    "lpso.info.to_excel('Leave_p-subject_Out.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, the classification is applied on each group. The mf parameter of the fit() function is just a shortcut to say that all features have to be consider together. \n",
    "U can use the grp parameter for:\n",
    "- Grouping features together\n",
    "- Labelize each one/group to have nice tables and plot"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
